Abstract Researchers continually upload data into public repositories at a rapid pace, yet utilize few common standards for annotation, making it close to impossible to compare or associate data across studies. To address this problem, we will develop a defined meta- data model and build an integrated system called Phenotype Discovery (PhD) that enables researchers to query and find genomic studies of interest in public repositories as well as upload new data into our database (sdGaP), in a standardized manner. A Query Interpreter (QI) will utilize text mining and natural language processing techniques to map free text into concepts in biomedical ontologies, allowing non-structured queries to be answered efficiently. In Phase I of the project, we will develop a proof-of-concept system that can retrospectively structure phenotypic descriptions in dbGaP, and will work with domain experts in pneumology to build use cases and evaluate the automated mappings. In Phase II of the project, we will extend the domain expertise to cardiology, hematology, and sleep disorders to build a more comprehensive system, expanding the phenotype annotation to transcriptome databases, and integrating a flexible automated genotype annotation tool for sdGaP. We will develop a user-friendly interface to prospectively assist researchers in uploading their data with standardized phenotypic annotations. We will provide the tool for free from our website and continuously improve its quality, based on user feedback and usage data.